Skylar Connor Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

ORISE Post Doctoral Fellow

National Center for Toxicological Research

postdoc

4 h-index 8 pubs 60 cited

Research Areas

Pharmacological Effects and Toxicity Studies Advanced Neural Network Applications Carcinogens and Genotoxicity Assessment Drug Transport and Resistance Mechanisms Metabolism and Genetic Disorders Health and Medical Research Impacts Natural Language Processing Techniques

Links

ORCID

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OverviewAI-generated summary

Skylar Connor, an ORISE Post Doctoral Fellow at the National Center for Toxicological Research, investigates the application of artificial intelligence (AI) in regulatory science, particularly concerning drug-induced toxicities. Their recent publications explore the generation of drug-induced renal injury lists to support the development of new approach methodologies for nephrotoxicity and the adaptability of AI for safety evaluation, using drug-induced liver injury as a case study. Connor has also contributed to research on drug-induced kidney injury, discussing associated challenges and opportunities. Their work includes the development of databases like DILIrank 2.0 for drug-induced liver injury risk and DICE for AI-based indication extraction. Connor's research network includes collaborators Weida Tong, Leihong Wu, and Minjun Chen from the National Center for Toxicological Research, with whom they have co-authored multiple publications. Connor's scholarship metrics include an h-index of 4, with 8 total publications and 60 total citations.

Metrics

  • h-index: 4
  • Publications: 8
  • Citations: 60

Selected Publications

  • DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review (2025) DOI
  • Is ChatGPT Ready for Public Use in Organ-Specific Drug Toxicity Research? (2025) DOI
  • Drug-induced kidney injury: challenges and opportunities (2024) DOI
  • Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity (2024) DOI
  • Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury (2022) DOI
  • Best practice and reproducible science are required to advance artificial intelligence in real-world applications (2022) DOI
  • DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction (2021) DOI

Collaborators

Researchers in the database who share publications

Weida Tong National Center for Toxicological Research 3 shared publications Leihong Wu National Center for Toxicological Research 1 shared publication Minjun Chen National Center for Toxicological Research 1 shared publication

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